Forest-RK: A new random forest induction method

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5227 LNAI, Issue , 2008, Pages 430-437

  Bernard, Simon ^a   Heutte, Laurent ^a   Adam, Sébastien ^a  

^a UNIVERSITÉ DE ROUEN   (France)

Author keywords

Bagging; Classification; Classifier Combination; Classifier Ensemble; Decision Trees; Random Forests

Indexed keywords

ARTIFICIAL INTELLIGENCE;

A-PRIORI; ARBITRARY VALUES; BAGGING; CLASSIFICATION; CLASSIFIER COMBINATION; CLASSIFIER ENSEMBLE; DECISION TREES; GREEDY SEARCH; HYPERPARAMETER; INDUCTION METHOD; INTELLIGENT COMPUTING; INTERNATIONAL CONFERENCES; PARAMETRIZATION; RANDOM FORESTS; RF INDUCTION; TREE INDUCTION;

SEISMIC PROSPECTING;

EID: 53049101306     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85984-0_52     Document Type: Conference Paper

References (15)

1. Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123-140 (1996)
2. Ho, T.: The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832-844 (1998)
3. Bernard, S., Heutte, L., Adam, S.: Using Random Forests for Handwritten Digit Recognition. In: International Conference on Document Analysis and Recognition, pp. 1043-1047 (2007)
4. Boinee, P., Angelis, A.D., Foresti, G.: Meta Random Forests. International Journal of Computationnal Intelligence 2(3), 138-147 (2005)
5. Breiman, L.: Random Forests. Machine Learning 45(1), 5-32 (2001)
6. Breiman, L.: Consistency of Random Forests and Other Averaging Classifiers. Technical Report (2004)
7. Cutler, A., Zhao, G.: Pert - Perfect Random Tree Ensembles. Computing Science and Statistics 33 (2001)
8. Geurts, P., Ernst, D., Wehenkel, L.: Extremely Randomized Trees. Machine Learning 36(1), 3-42 (2006)
9. Latirme, P., Debeir, O., Decaestecker, C.: Limiting the Number of Trees in Random Forests. In: 2nd International Workshop on Multiple Classifier Systems, pp. 178-187 (2001)
10. Rodriguez, J., Kuncheva, L., Alonso, C.: Rotation forest: A New Classifier Ensemble Method. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(10) (2006)
11. Robnik-Sikonja, M.: Improving Random Forests. In: CSL 2004. LNCS (LNAI), vol. 3210, pp. 359-370. Springer, Berlin (2004)
12. Dietterich, T.: Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation 10, 1895-1923 (1998)
13. Everitt, B.: The Analysis of Contingency Tables. Chapman and Hall, London (1977)
14. Asuncion, A., Newman, D.: UCI Machine Learning Repository (2007)
15. Breiman, L.: Arcing classifiers. The Annals of Statistics 26(3), 801-849 (1998)